Data Mining (guided self study)

This course focuses on concepts and methods for frequent pattern discovery, also known as association analysis. This edition of the course is a structured and guided self-study course with weekly tasks and supervision, with mandatory attendance. Prerequisites: BSc degree and the course Introduction to Machine Learning or equivalent. Course book: Tan P., Steinbach M. & Kumar V.: Introduction to Data Mining, Chapters 6 and 7. Addison Wesley, 2006.

Lectures

Time

Room

Lecturer

Date

Fri 10-12

B222

Hannu Toivonen

20.01.2017-03.03.2017

Information for international students

This course is given in English.

General

This course will familiarize the participants with concepts and methods for identifying interesting patterns from large datasets. Data mining is about trying to make sense of data, usually without clear questions or clear success criteria. The course will focus on discovery of frequent patters in data, a fundamental data mining task that can help extract knowledge and previously unknown patterns also from largely unstructured data.

Completing the course

This instance of the course is based on self studies, according to a given study schedule and supported by weekly mentoring by the professor. Mentoring is based on so-called flipped classroom: students study the material first, and the meetings on Fridays are used to answer questions by the students, fill the gaps etc.

The course is completed solely by taking a final exam on 10 March 2017 (or 25 April). Check out https://www.cs.helsinki.fi/en/exams for possible changes on exam schedules. Participation in Friday sessions in voluntary. There are no exercise sessions.

NEW (24 Mar 2017): The exam has been graded and results are available at https://ilmo.cs.helsinki.fi/tulokset/studies. You should be able to see your points for each task in the exam. If you have any questions, contact Hannu by dropping in in his lab (rooms B233/B232) wihtout an appointment. Good times to find him: Wed (29 Mar) 9:30-12, Thu (30 Mar) 14-16, Fri (31 Mar) 10-14.

Schedule

The following topics are to be studied before the respective meeting date. The meetings are based on students' needs, not on planned lectures.